In a previous post, I talked about the parallels between the scientific method and user centered design, and described how the first step in the scientific method – characterization – is just like the up-front user observation that is the hallmark of many user centered design processes.
So what about the next steps in the scientific method?
As any engineer knows, the next crucial steps after observation are the creation of a hypothesis to explain the observed phenomena, and the use of the hypothesis to predict new observations not yet made.
The analogy in design is prototyping.
Think about it. A prototype is really nothing more than a physical manifestation of a hypothesis—a hypothesis about underlying user needs based on observation, and a particular product or service solution to address these needs.
Now, designers and engineers often get into trouble over what constitutes a “prototype.” In the analogy I’m drawing, I’m referring to conceptual prototypes, which are meant to convey the core product concept to the user and present it in such a way that feedback can be gathered in the field—in the real context where the product will be used. This type of prototype should be low fidelity—perhaps even paper, cardboard or foamcore—and imply malleability to the customer. Because of their low fidelity nature, prototypes like this can be created at very low cost and very quickly. This is not the same animal at all as a technical prototype, which is created for proof of feasibility, manufacturability, or to measure performance.
It’s here that some of my more hardcore technical friends fault designers. “After all of that effort to observe users and model the observations,” they say, “your concepts just seem to come out of thin air!” Indeed, where DO these ideas really come from?
Since I’m developing an analogy here, let me answer an analogous question: where do scientific hypotheses come from? At the core, scientists make hypotheses based on the observations they’ve gathered, their experience and knowledge of their particular scientific domain, and a quite crucial capacity—their ability to intuit patterns in the observed data. In fact, what separates good scientists from great scientists is this ability to see patterns and correlations where others do not. Thousands of more mediocre scientists might have overlooked what was happening in that Petri dish on the windowsill, but Fleming combined what he saw with what he knew about germ theory and hypothesized that the quite special mold he saw killed Staphylococcus aureus.
It is this moment of discovery, this flash of intuition, that can never be reduced to process—no matter how “repeatable” and “objective” the scientific method is claimed to be, this moment of hypothesis creation remains ineffable, impossible to predict. It is THE crucial creative step.
So let’s go back and answer the question of the origin of design concepts—where DO the ideas come from? They come from the same raw material that scientists use for hypothesis creation: observations of user behavior, values and attitudes, experience and knowledge in the design domain and the same ability to intuit patterns in the observed data. Good designers are separated from great designers primarily in this capacity.
And the “mystery” of where ideas come from is no more “magical” in design than it is in science.
But—if the intuitive, creative step is the key one in the process—why not just get one really smart genius person to invent for you? This is a valid question and deserves consideration.
The answer to innovation, according to this “lone genius” line of thinking, is that a few really smart guys unmolested by process and management come up with the best ideas… so just fund them and get the hell out of the way.
While it’s undeniable that the garage visionaries have indeed created great products and even entire industries, the unfortunate problem for most companies is that garage visionaries are in exceedingly short supply. Absent a resident genius, if you’re a company interested in innovation the real problem of innovation comes down to maximizing the probability of great ideas, given your resources.
In other words, while you’re busy thinking like a scientist, you also need to think like a bookie. And the bookie in you should want to stack the odds in your favor. So how do you do that?
You can actually stack the odds in two ways. The first way is to get a diverse set of brains working on the creation of ideas—in essence, enlarge the expertise and domain knowledge available to the creative process. This means cross-functionality, engineering as well as marketing, product management as well as supply chain. Too many companies still implement “research” as a siloed group of engineers or developers, oftentimes separated by distance and org chart span from development, marketing and other operational groups. This in fact inhibits practical creativity. Instead, you should try to get as many functions and skills to participate in idea creation as you can.
In his delightful new book Imagine: How Creativity Works, Jonah Lehrer describes how Steve Jobs personally intervened in the design of Pixar’s campus to encourage people to mix. He demanded that Pixar’s cartoonists, developers and engineers sit in a single building, with one large space. He even mandated that there be only one set of restrooms. As Lehrer recounts to NPR,
“He wanted there to be mixing. He knew that the human friction makes the sparks, and that when you're talking about a creative endeavor that requires people from different cultures to come together, you have to force them to mix; that our natural tendency is to stay isolated, to talk to people who are just like us, who speak our private languages, who understand our problems. But that's a big mistake. And so his design was to force people to come together even if it was just going to be in the bathroom."
The second odds stacking technique is to immerse your cross-functional team’s minds in the data you’ve collected about your customers. Bathed in data about your customers’ real-world behaviors, attitudes and values, they can recognize useful patterns, non-obvious matches between needs and your company’s existing or potential ability to meet them. By creating this potent mix of a cross functional team—and its resulting diverse thinking—with real user data, you improve your odds that truly innovative hypotheses are created in that crucial creative step.
You have stacked the deck in your favor.
So there you have it: user observation is like scientific characterization of phenomena, and prototypes are physical manifestations of hypotheses about how technology can address the underlying needs identified in the observations. Both have crucial creative steps that cannot be reduced to process, and neither is really more magical or unstructured than the other.
In my last post on this topic, I’ll wrap up my science/user centered design analogy by describing how we should think about “testing” our prototyped design hypotheses. We’ll see that we are NOT talking about quantitative, design-of-experiments, six sigma-type experimentation. We are talking about qualitative evaluation of conceptual stimuli. There is a huge difference. Stay tuned.